Automated tactics planning for cyber attack and defense based on large language model agents

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Network Security

Background:

• Cyber incidents are increasing in complexity and frequency.
• Traditional cybersecurity tactics planning is insufficient for timely and effective responses.
• Advanced, automated solutions are crucial for both attackers and defenders.

Purpose of the Study:

• To explore Large Language Models (LLMs) from a Reinforcement Learning (RL) perspective for automated cybersecurity tactics planning.
• To develop more complex and adaptive tactics for cyber attack and defense scenarios.
• To leverage LLMs' natural language processing capabilities for strategic planning.

Main Methods:

• Constructed a publicly available simulation environment for cyber attack and defense.
• Proposed tactic agents utilizing LLMs within an RL framework.
• Conducted extensive experiments (nearly a thousand) within the simulation environment.

Main Results:

• The proposed LLM-based tactic agents demonstrated significant improvements in automated tactics planning.
• Experimental results verified enhanced effectiveness and adaptability in cyber attack and defense scenarios.
• The approach offers a promising direction for advancing automated cybersecurity strategies.

Conclusions:

• LLMs, when integrated with RL, provide a powerful framework for automated cybersecurity tactics.
• The developed agents show potential for creating more sophisticated and responsive cyber defense and attack strategies.
• This research paves the way for future innovations in AI-driven cybersecurity.